{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:IO4XAFUS5IBR2QMFCVXIO4QWNG","short_pith_number":"pith:IO4XAFUS","schema_version":"1.0","canonical_sha256":"43b9701692ea031d4185156e877216698b09bd9e35b04ef82568d4dfa917c9b0","source":{"kind":"arxiv","id":"2303.01296","version":1},"attestation_state":"computed","paper":{"title":"Optimal Rates and Efficient Algorithms for Online Bayesian Persuasion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.GT","authors_text":"Alberto Marchesi, Andrea Celli, Francesco Trov\\`o, Martino Bernasconi, Matteo Castiglioni, Nicola Gatti","submitted_at":"2023-03-02T14:23:13Z","abstract_excerpt":"Bayesian persuasion studies how an informed sender should influence beliefs of rational receivers who take decisions through Bayesian updating of a common prior. We focus on the online Bayesian persuasion framework, in which the sender repeatedly faces one or more receivers with unknown and adversarially selected types. First, we show how to obtain a tight $\\tilde O(T^{1/2})$ regret bound in the case in which the sender faces a single receiver and has partial feedback, improving over the best previously known bound of $\\tilde O(T^{4/5})$. Then, we provide the first no-regret guarantees for the"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2303.01296","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GT","submitted_at":"2023-03-02T14:23:13Z","cross_cats_sorted":[],"title_canon_sha256":"5f86e4d2657653884ff4af8fe18da8aaf09f2e0769aea2386c744ed7c0aa0084","abstract_canon_sha256":"850eaa4b1e7f79906544010df93088dbdabeca1bcac9e6f1bb70e409d47d2f68"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:47:29.299895Z","signature_b64":"SVsqKqLDsob7HYZM9CEJgUFJ7V2Nrw1eAUq3Ft9kA27Gfz08+RhhW7/L/zwJW9kk7Hzx50pjqoK2RqckQ8p9Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"43b9701692ea031d4185156e877216698b09bd9e35b04ef82568d4dfa917c9b0","last_reissued_at":"2026-07-05T05:47:29.299484Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:47:29.299484Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimal Rates and Efficient Algorithms for Online Bayesian Persuasion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.GT","authors_text":"Alberto Marchesi, Andrea Celli, Francesco Trov\\`o, Martino Bernasconi, Matteo Castiglioni, Nicola Gatti","submitted_at":"2023-03-02T14:23:13Z","abstract_excerpt":"Bayesian persuasion studies how an informed sender should influence beliefs of rational receivers who take decisions through Bayesian updating of a common prior. We focus on the online Bayesian persuasion framework, in which the sender repeatedly faces one or more receivers with unknown and adversarially selected types. First, we show how to obtain a tight $\\tilde O(T^{1/2})$ regret bound in the case in which the sender faces a single receiver and has partial feedback, improving over the best previously known bound of $\\tilde O(T^{4/5})$. Then, we provide the first no-regret guarantees for the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.01296","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2303.01296/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2303.01296","created_at":"2026-07-05T05:47:29.299547+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.01296v1","created_at":"2026-07-05T05:47:29.299547+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.01296","created_at":"2026-07-05T05:47:29.299547+00:00"},{"alias_kind":"pith_short_12","alias_value":"IO4XAFUS5IBR","created_at":"2026-07-05T05:47:29.299547+00:00"},{"alias_kind":"pith_short_16","alias_value":"IO4XAFUS5IBR2QMF","created_at":"2026-07-05T05:47:29.299547+00:00"},{"alias_kind":"pith_short_8","alias_value":"IO4XAFUS","created_at":"2026-07-05T05:47:29.299547+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.17379","citing_title":"Provably Efficient Algorithm for Best Scoring Rule Identification in Online Principal-Agent Information Acquisition","ref_index":10,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IO4XAFUS5IBR2QMFCVXIO4QWNG","json":"https://pith.science/pith/IO4XAFUS5IBR2QMFCVXIO4QWNG.json","graph_json":"https://pith.science/api/pith-number/IO4XAFUS5IBR2QMFCVXIO4QWNG/graph.json","events_json":"https://pith.science/api/pith-number/IO4XAFUS5IBR2QMFCVXIO4QWNG/events.json","paper":"https://pith.science/paper/IO4XAFUS"},"agent_actions":{"view_html":"https://pith.science/pith/IO4XAFUS5IBR2QMFCVXIO4QWNG","download_json":"https://pith.science/pith/IO4XAFUS5IBR2QMFCVXIO4QWNG.json","view_paper":"https://pith.science/paper/IO4XAFUS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.01296&json=true","fetch_graph":"https://pith.science/api/pith-number/IO4XAFUS5IBR2QMFCVXIO4QWNG/graph.json","fetch_events":"https://pith.science/api/pith-number/IO4XAFUS5IBR2QMFCVXIO4QWNG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IO4XAFUS5IBR2QMFCVXIO4QWNG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IO4XAFUS5IBR2QMFCVXIO4QWNG/action/storage_attestation","attest_author":"https://pith.science/pith/IO4XAFUS5IBR2QMFCVXIO4QWNG/action/author_attestation","sign_citation":"https://pith.science/pith/IO4XAFUS5IBR2QMFCVXIO4QWNG/action/citation_signature","submit_replication":"https://pith.science/pith/IO4XAFUS5IBR2QMFCVXIO4QWNG/action/replication_record"}},"created_at":"2026-07-05T05:47:29.299547+00:00","updated_at":"2026-07-05T05:47:29.299547+00:00"}